Semiconductor integrated circuits (ICs) are fabricated by an extremely complex process that involves several hundred or more operations. They are fabricated by selectively implanting impurities into, and applying conductive and insulative layers onto, a semiconductor substrate. Semiconductor ICs (i.e., "chips") are not manufactured individually, but rather as an assembly of a hundred or more chips on a "wafer," which is then diced up to produce the individual chips.
Increasing production yield is an ongoing problem in the manufacture of semiconductor chips. Because of various defects that can occur in fabrication of a wafer, significant numbers of wafer die have to be discarded for one reason or another, thereby decreasing the percentage yield per wafer and driving up the cost of individual chips. Defects are typically caused by foreign particles, minute scratches and other imperfections introduced during photoresist, photomask and diffusion operations. Yield impacts the number of wafer starts at the inception of production needed to meet specific customer order quantities for finished chips at the end of the production line. With the high demand for semiconductor chips and more orders than can possibly be filled by a fabrication facility, predicting yield to accurately gauge wafer starts, and utilizing defect information to remove yield detracting operations, are important aspects of improving the efficiency, and hence output, of the fabrication facility.
Wafer scanning tools are utilized to identify defects that occur in the chip manufacturing process for the aforementioned purposes. Typically, such tools are located at a variety of positions along the production line and comprise automated-vision inspection stations for identifying visual irregularities in the wafer die as they move through the line. The irregularities, i.e., defects, are recorded according to their coordinates, estimate of size or other parameters and are stored as records in a database. The records represent raw information that must then be analyzed or otherwise processed off line to determine the impact, if any, of the identified defects on product yield. Some defects, for example, do not adversely affect yield as much as others, and therefore must be classified differently for analysis purposes.
Commercially available wafer scanning tools include those made by KLA Instruments Corporation of Santa Clara, Calif., Tencor Instruments Corporation of Mountain View, Calif., Inspex, Inc. of Billerica, Mass. and numerous other manufacturers. Despite significant advances made in wafer scanning technology, the various tools that are available suffer striking deficiencies. In particular, such tools lack the capability to perform certain advanced classification and analysis of defect information necessary to accurately determine the true impact of wafer defects on yield. While conventional tools offer simple data presentation capabilities, such as the display of wafer maps, histograms and charts, they do not adequately classify or process the defect data. Classification codes typically must be manually entered into wafer scan records indicating the type of defect and its potential impact on yield. This is done by placing the wafer on a review station where a user makes a judgment as to what the defect identified by the scan tool is, enters a classification code in the record for that defect, then proceeds to the next defect.
More sophisticated tools perform limited automatic classification of defect records according to the location of the defect on the wafer, in an effort to remove consideration of defects that do not require any classification. For example, it is recognized that a number of defect points in close proximity to one another, collectively referred to as a "cluster," can indicate a single event such as a contaminant or scratch present across one or more die. The sometimes thousands of defect points comprising a cluster therefore inordinately skew the data, representing "noise" that should be treated as a smaller number of defects for yield determination purposes. Therefore, some tools include the ability to automatically "decluster" the defect data, i.e., convert a cluster containing potentially many defect points to a single or smaller number of defects. Unfortunately, however, decluster algorithms typically used with known tools tend to be specific to a particular scan tool and are not readily adaptable for use with other tools.
Another disadvantage suffered by scanning tools is that they do not adequately perform other types of automatic classification or yield prediction operations beneficial in a manufacturing defect analysis, thereby limiting the utility thereof. For example, in addition to the declustering of defect data, it is often desirable to further refine the defect data before manual inspection and classification of individual defects on the review station. Since each wafer can include so many defects it would not be practical to manually review and classify each of them, it would be desirable to utilize a method to randomly choose a statistically meaningful sample, i.e., subset, of such defects for consideration.
Historically, the review station operator randomly picks sets of defects that seem interesting and then reviews and classifies them. However, it is difficult for humans to systematically choose defects for this purpose that will be representative of all of the defects on the wafer. Some review stations are equipped with the ability to randomly move to different defects which the operator can then review and classify. A problem though with conventional randomizing methods performed on review stations is that they are not necessarily accurate in representing a true sampling of the wafer. For example, picking defects at random tends to result in the inordinate picking of defects that are part of a big cluster, because there are more of them, while defects of other types and in other locations on the wafer are overlooked. Therefore, it would be desirable to adopt an automated, consistent method for randomly identifying for review, i.e., "preclassifying," defects of interest. The methodology used to randomly identify, i.e., preclassify, defects preferably would focus on defect subpopulations defined in terms of defect size ranges, or alternatively in terms of locations on the wafer, so that the sample of defects chosen best reflects conditions actually occuring on the wafer.
Once defects of interest are randomly preclassified according to a beneficial methodology, they can then be manually reviewed and assigned appropriate classification codes by an operator to indicate defect type, e.g., big cluster, small cluster, or the like. Some equipment is available that automatically assigns such classification codes to defect types based upon machine visual attributes.
After defects are randomly chosen (i.e., preclassified), and then reviewed to assign classification codes thereto, the next task of interest is how to use the sample to estimate what the classification codes should be for the remaining defects on the wafer. The classification codes for the entire wafer should be extrapolated from the sample defect data consistent with the preclassification methodology used earlier to sample the subpopulations of defects. Finally, the estimated, i.e., extrapolated, defect data with classification code estimates for all defects, can be analyzed to provide warnings during manufacturing of certain adverse conditions.
Unfortunately, known defect analysis systems do not contemplate preclassification, review and extrapolation methodologies of the foregoing type for accurately and efficiently analyzing semiconductor defect data. Instead, known systems tend to rely simplistic, line of regression analyses, or other statistical correlations of defects to yield for a given lot or given wafer. More sophisticated systems are deficient in accounting for spacially inhomogeneous distributions of defect types and in extrapolating defect type information of the entire population from a small sample of reviewed defects.
Consequently, there is a need for a method and system that permits more accurate defect analysis to be performed in cooperation with semiconductor wafer scanning tools.
Particularly, there is a need for a method and system that performs an automatic preclassification of defect data based upon consideration of defects occuring in different defect subpopulations, rather than by simple randomization of defects, to identify a statistically meaningful sample of defects to be analyzed.
There is further a need for a method and system that performs extrapolation of sampled defect data in a way that assigns classification codes to unreviewed defects consistent with the preclassification scheme used to identify defects in different defect subpopulations.